Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "122" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 30 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 28 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459846 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 11.873618 | 10.772426 | 0.535851 | 0.353208 | 1.161838 | -0.715829 | 0.906229 | -0.776305 | 0.8530 | 0.6960 | 0.4739 | 3.397692 | 2.514401 |
| 2459845 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459844 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459842 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 3.451461 | 5.252821 | 3.239215 | 1.032939 | -0.401029 | 0.438647 | -0.040687 | 0.157289 | 0.7586 | 0.7135 | 0.2437 | 4.690704 | 4.170410 |
| 2459841 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459840 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 64.397293 | 66.179567 | 8.436532 | 5.938757 | 3.403010 | 2.963029 | 10.946259 | 8.305605 | 0.0213 | 0.0214 | 0.0004 | nan | nan |
| 2459839 | digital_ok | 100.00% | - | - | - | - | - | 16.369120 | 17.144719 | 19.972820 | 14.510614 | 4.308745 | 1.817786 | 15.019660 | 10.610010 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 6.749563 | 6.231210 | 7.328814 | 1.603513 | -0.531235 | 0.303864 | -0.408758 | -0.783899 | 0.7572 | 0.7392 | 0.3750 | 4.936652 | 4.583352 |
| 2459835 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459833 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459832 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 13.868050 | 10.670390 | 0.892009 | 0.263279 | 1.095277 | -0.162652 | -0.422933 | -0.936179 | 0.8274 | 0.5826 | 0.5610 | 4.382992 | 3.452144 |
| 2459831 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 16.420230 | 17.430902 | 21.210294 | 15.533619 | 4.148305 | 3.625637 | 10.178740 | 8.196339 | 0.0219 | 0.0229 | 0.0011 | nan | nan |
| 2459830 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 12.878295 | 11.883761 | 1.283817 | 0.945629 | -1.471225 | -0.687794 | 0.367298 | 0.298506 | 0.8274 | 0.5948 | 0.5438 | 6.377111 | 5.247649 |
| 2459829 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 16.948846 | 13.365645 | 0.524485 | 1.150128 | 1.645627 | -0.527269 | 1.148417 | 0.515199 | 0.7777 | 0.7036 | 0.3912 | -0.000000 | -0.000000 |
| 2459828 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 10.851952 | 9.622128 | 0.465991 | 0.736978 | -0.560515 | -0.089811 | -0.146665 | -1.003435 | 0.8248 | 0.6049 | 0.5263 | 0.000000 | 0.000000 |
| 2459827 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 12.894335 | 11.174520 | 1.425591 | 1.887365 | 0.698904 | -0.974596 | -0.561465 | -0.954810 | 0.7853 | 0.7113 | 0.3940 | 5.287544 | 4.597474 |
| 2459826 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 9.604653 | 8.902226 | 1.722419 | 1.622153 | -0.133389 | 1.201413 | -0.138706 | -0.284811 | 0.8241 | 0.6214 | 0.4995 | 0.000000 | 0.000000 |
| 2459825 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 9.707980 | 8.862374 | 0.826567 | 0.319633 | -0.753569 | -0.700291 | -0.588731 | -0.703900 | 0.8279 | 0.6459 | 0.4900 | 3.713285 | 3.079478 |
| 2459824 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 11.136881 | 8.491442 | 0.917719 | 0.666068 | -0.215977 | -0.939857 | 0.021046 | -0.285394 | 0.7613 | 0.7757 | 0.3289 | 3.466231 | 3.404772 |
| 2459823 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 8.134794 | 7.107214 | 1.633201 | 1.127315 | 1.184904 | -0.022693 | 1.219938 | 1.421897 | 0.7940 | 0.6882 | 0.4372 | 8.950929 | 3.816369 |
| 2459822 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 9.859128 | 9.164591 | 1.217182 | 1.237439 | -0.366396 | 0.074455 | 0.253659 | 0.777471 | 0.8242 | 0.6595 | 0.4881 | 6.194340 | 4.868198 |
| 2459821 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 9.770131 | 9.148606 | 1.137084 | 1.439498 | -1.320938 | -0.330954 | -1.524006 | -0.983546 | 0.8244 | 0.6699 | 0.4848 | 3.330433 | 2.894288 |
| 2459820 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 12.681101 | 10.684102 | 1.113501 | 1.744426 | 0.716192 | -0.159959 | 0.925632 | 0.967738 | 0.7973 | 0.7166 | 0.3897 | 6.269754 | 5.235428 |
| 2459817 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 9.299765 | 7.979678 | 0.695269 | 0.974410 | -0.674557 | -0.239194 | -0.557095 | -0.638414 | 0.8258 | 0.6994 | 0.4856 | 3.255052 | 3.005672 |
| 2459816 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 9.959407 | 7.931058 | 1.697313 | 0.805615 | -0.405549 | 0.176679 | 0.900059 | 0.516718 | 0.8630 | 0.6424 | 0.5632 | 4.618745 | 3.792424 |
| 2459815 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 8.515087 | 7.327675 | 1.446847 | 0.980544 | 0.005883 | -0.062579 | 0.110202 | 0.427404 | 0.8267 | 0.7140 | 0.4897 | 4.475268 | 4.007532 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 16.781185 | 14.795263 | 0.557961 | 1.323179 | 0.925682 | 1.195292 | 0.647082 | 1.007496 | 0.0909 | 0.0837 | 0.0178 | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 11.873618 | 11.873618 | 10.772426 | 0.535851 | 0.353208 | 1.161838 | -0.715829 | 0.906229 | -0.776305 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | nn Shape | 5.252821 | 3.451461 | 5.252821 | 3.239215 | 1.032939 | -0.401029 | 0.438647 | -0.040687 | 0.157289 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | nn Shape | 66.179567 | 64.397293 | 66.179567 | 8.436532 | 5.938757 | 3.403010 | 2.963029 | 10.946259 | 8.305605 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Power | 19.972820 | 17.144719 | 16.369120 | 14.510614 | 19.972820 | 1.817786 | 4.308745 | 10.610010 | 15.019660 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Power | 7.328814 | 6.231210 | 6.749563 | 1.603513 | 7.328814 | 0.303864 | -0.531235 | -0.783899 | -0.408758 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 13.868050 | 13.868050 | 10.670390 | 0.892009 | 0.263279 | 1.095277 | -0.162652 | -0.422933 | -0.936179 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Power | 21.210294 | 16.420230 | 17.430902 | 21.210294 | 15.533619 | 4.148305 | 3.625637 | 10.178740 | 8.196339 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 12.878295 | 12.878295 | 11.883761 | 1.283817 | 0.945629 | -1.471225 | -0.687794 | 0.367298 | 0.298506 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 16.948846 | 13.365645 | 16.948846 | 1.150128 | 0.524485 | -0.527269 | 1.645627 | 0.515199 | 1.148417 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 10.851952 | 9.622128 | 10.851952 | 0.736978 | 0.465991 | -0.089811 | -0.560515 | -1.003435 | -0.146665 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 12.894335 | 12.894335 | 11.174520 | 1.425591 | 1.887365 | 0.698904 | -0.974596 | -0.561465 | -0.954810 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 9.604653 | 8.902226 | 9.604653 | 1.622153 | 1.722419 | 1.201413 | -0.133389 | -0.284811 | -0.138706 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 9.707980 | 8.862374 | 9.707980 | 0.319633 | 0.826567 | -0.700291 | -0.753569 | -0.703900 | -0.588731 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 11.136881 | 11.136881 | 8.491442 | 0.917719 | 0.666068 | -0.215977 | -0.939857 | 0.021046 | -0.285394 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 8.134794 | 7.107214 | 8.134794 | 1.127315 | 1.633201 | -0.022693 | 1.184904 | 1.421897 | 1.219938 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 9.859128 | 9.859128 | 9.164591 | 1.217182 | 1.237439 | -0.366396 | 0.074455 | 0.253659 | 0.777471 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 9.770131 | 9.148606 | 9.770131 | 1.439498 | 1.137084 | -0.330954 | -1.320938 | -0.983546 | -1.524006 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 12.681101 | 12.681101 | 10.684102 | 1.113501 | 1.744426 | 0.716192 | -0.159959 | 0.925632 | 0.967738 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 9.299765 | 9.299765 | 7.979678 | 0.695269 | 0.974410 | -0.674557 | -0.239194 | -0.557095 | -0.638414 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 9.959407 | 7.931058 | 9.959407 | 0.805615 | 1.697313 | 0.176679 | -0.405549 | 0.516718 | 0.900059 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 8.515087 | 7.327675 | 8.515087 | 0.980544 | 1.446847 | -0.062579 | 0.005883 | 0.427404 | 0.110202 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 122 | N08 | digital_ok | ee Shape | 16.781185 | 14.795263 | 16.781185 | 1.323179 | 0.557961 | 1.195292 | 0.925682 | 1.007496 | 0.647082 |